---
title: "NEON4CAST Dashboard"
output:
  flexdashboard::flex_dashboard:
    theme: 
      version: 4
      bootswatch: solar
    orientation: columns
    vertical_layout: fill
    source_code: embed
    social: menu
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(clock)
source("R/plotly_helpers.R")
```


Home
=====



```{r include=FALSE}
combined <- read_csv("https://data.ecoforecast.org/analysis/combined_forecasts_scores.csv")
```


Column {data-width=650}
-----------------------------------------------------------------------

Column {data-width=350}
-----------------------------------------------------------------------


### Teams

```{r}
total <- combined %>% select(team) %>% distinct() %>% count()
flexdashboard::valueBox(total)
```






Phenology
==========


Column {data-width=650}
-----------------------------------------------------------------------

### Phenology

```{r}
## determine these more cleverly
start <- as.Date("2021-05-01")
end <- start %>% clock::add_months(1)


p <- combined %>%
  filter(theme == "phenology",
         target == "gcc_90",
         forecast_start_time ==start,
         time < end) %>% 
  ggplot() +
  geom_ribbon(aes(x = time, ymin = lower95, ymax = upper95, fill = team), alpha = 0.2) +
  geom_line(aes(time, mean, col = team)) +
  geom_point(aes(time, obs)) + 
  facet_wrap(~siteID)

gp <- plotly::ggplotly(p)
gp <- patch_legend(gp)

gp
```

Column {data-width=350}
-----------------------------------------------------------------------

### Forecast Submissions

```{r}
gauge(42, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(80, 100), warning = c(40, 79), danger = c(0, 39)
))
```

### Teams

```{r}
total <- combined %>% filter(theme == "phenology") %>% select(team) %>% distinct() %>% count()
flexdashboard::valueBox(total)
```

### Leaderboard

```{r}
combined %>% 
  filter(theme == "phenology") %>%
  group_by(team) %>%
  summarise(mean_crps = mean(crps,na.rm=TRUE)) %>%
  arrange(mean_crps) %>%
  rmarkdown::paged_table()
```


Aquatics
========

Column {data-width=650}
-----------------------------------------------------------------------

```{r}

## Could consider displaying older ones
start <- combined %>% 
  filter(theme == "aquatics") %>%
  select(forecast_start_time) %>% 
  distinct() %>% 
  arrange(desc(forecast_start_time))

p <- combined %>%
  filter(theme == "aquatics", forecast_start_time == start[[2,1]]) %>%
  ggplot() +
  geom_ribbon(aes(x = time, ymin = lower95, ymax = upper95, fill = team), alpha = 0.2) +
  geom_line(aes(time, mean, col = team)) +
  geom_point(aes(time, obs)) + 
  facet_wrap(target~siteID, scales = "free")

gp <- plotly::ggplotly(p)
gp <- patch_legend(gp)

gp
```

Column {data-width=350}
-----------------------------------------------------------------------


### Leaderboard

```{r}
combined %>% 
  filter(theme == "aquatics") %>%
  group_by(team) %>%
  summarise(mean_crps = mean(crps,na.rm=TRUE)) %>%
  arrange(mean_crps) %>%
  rmarkdown::paged_table()
```

Terrestrial
===========

Column {data-width=650}
-----------------------------------------------------------------------

```{r}
## Could consider displaying older ones
start <- combined %>% 
  filter(theme == "terrestrial_daily") %>%
  select(forecast_start_time) %>% 
  distinct() %>% 
  arrange(desc(forecast_start_time))

p <- combined %>%
  filter(theme == "terrestrial_daily", forecast_start_time == start[[2,1]]) %>%
  ggplot() +
  geom_ribbon(aes(x = time, ymin = lower95, ymax = upper95, fill = team), alpha = 0.2) +
  geom_line(aes(time, mean, col = team)) +
  geom_point(aes(time, obs)) + 
  facet_grid(target ~ siteID, scales = "free")

gp <- plotly::ggplotly(p)
gp <- patch_legend(gp)

gp
```



Column {data-width=350}
-----------------------------------------------------------------------


### Leaderboard

```{r}
combined %>% 
  filter(theme == "terrestrial_daily") %>%
  group_by(team) %>%
  summarise(mean_crps = mean(crps,na.rm=TRUE)) %>%
  arrange(mean_crps) %>%
  rmarkdown::paged_table()
```


Ticks
=======

Column {data-width=650}
-----------------------------------------------------------------------


```{r}
## Could consider displaying older ones
start <- combined %>% 
  filter(theme == "ticks") %>%
  select(forecast_start_time) %>% 
  distinct() %>% 
  arrange(desc(forecast_start_time))

p <- combined %>%
  filter(theme == "ticks", forecast_start_time == start[[2,1]]) %>% # second most recent start time
  ggplot() +
  geom_ribbon(aes(x = time, ymin = lower95, ymax = upper95, fill = team), alpha = 0.2) +
  geom_line(aes(time, mean, col = team)) +
  geom_point(aes(time, obs)) + 
  facet_grid(target ~ siteID)

gp <- plotly::ggplotly(p)
gp <- patch_legend(gp)

gp
```


Column {data-width=350}
-----------------------------------------------------------------------

### Leaderboard

```{r}
combined %>% 
  filter(theme == "ticks") %>%
  group_by(team) %>%
  summarise(mean_crps = mean(crps,na.rm=TRUE)) %>%
  arrange(mean_crps) %>%
  rmarkdown::paged_table()
```


Beetles
=======

Column {data-width=650}
-----------------------------------------------------------------------


```{r}
## determine these more cleverly
start <- combined %>% 
  filter(theme == "beetles") %>%
  select(forecast_start_time) %>% 
  distinct() %>% 
  arrange(desc(forecast_start_time))

p <- combined %>%
  filter(theme == "beetles", forecast_start_time == start[[1,1]]) %>% # second most recent start time
  ggplot() +
  geom_ribbon(aes(x = time, ymin = lower95, ymax = upper95, fill = team), alpha = 0.2) +
  geom_line(aes(time, mean, col = team)) +
  geom_point(aes(time, obs)) + 
  facet_wrap(~target)


gp <- plotly::ggplotly(p)
gp <- patch_legend(gp)

gp
```


Column {data-width=350}
-----------------------------------------------------------------------


### Leaderboard

```{r}
combined %>% 
  filter(theme == "beetles") %>%
  group_by(team) %>%
  summarise(mean_crps = mean(crps,na.rm=TRUE)) %>%
  arrange(mean_crps) %>%
  rmarkdown::paged_table()
```